Innovative engineering with AI applications /

Innovative Engineering with AI Applications Innovative Engineering with AI Applications demonstrates how we can innovate in different engineering domains as well as how to make most business problems simpler by applying AI to them. Engineering advancements combined with artificial intelligence (AI),...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Ahirwar, Anamika (Editor ), Shukla, Piyush Kumar, Shrivastava, Manish, Maheshwary, Priti, Gour, Bhupesh
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : Beverly, MA : John Wiley & Sons, Inc. ; Scrivener Publishing LLC, 2023.
Temas:
Artificial intelligence > Industrial applications.
Intelligence artificielle > Applications industrielles.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Introduction of AI in Innovative Engineering
  • 1.1 Introduction to Innovation Engineering
  • 1.2 Flow for Innovation Engineering
  • 1.3 Guiding Principles for Innovation Engineering
  • 1.4 Introduction to Artificial Intelligence
  • 1.4.1 History of Artificial Intelligence
  • 1.4.2 Need for Artificial Intelligence
  • 1.4.3 Applications of AI
  • 1.4.4 Comprised Elements of Intelligence
  • 1.4.5 AI Tools
  • 1.4.6 AI Future in 2035
  • 1.4.7 Humanoid Robot and AI
  • 1.4.8 The Explosive Growth of AI
  • 1.5 Types of Learning
  • 1.6 Categories of AI
  • 1.7 Branches of Artificial Intelligence
  • 1.8 Conclusion
  • Bibliography
  • Chapter 2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals
  • 2.1 Introduction
  • 2.2 Literature Review
  • 2.3 Dataset and Pre-Processing
  • 2.4 Machine Learning Techniques Used
  • 2.5 Performance Parameter
  • 2.6 Proposed Methodology
  • 2.7 Result and Experiment
  • 2.8 Comparison of Six Different Approaches For Stress Detection
  • 2.9 Conclusions
  • 2.10 Future Scope
  • References
  • Chapter 3 Deep Learning: Tools and Models
  • 3.1 Introduction
  • 3.1.1 Definition
  • 3.1.2 Elements of Neural Networks
  • 3.1.3 Tool: Keras
  • 3.2 Deep Learning Models
  • 3.2.1 Deep Belief Network [DBN]
  • 3.2.1.1 Fundamental Architecture of DBN
  • 3.2.1.2 Implementing DBN Using MNIST Dataset
  • 3.2.2 Recurrent Neural Network [RNN]
  • 3.2.2.1 Fundamental Architecture of RNN
  • 3.2.2.2 Implementing RNN Using MNIST Dataset
  • 3.2.3 Convolutional Neural Network [CNN]
  • 3.2.3.1 Fundamental Architecture of CNN
  • 3.2.3.2 Implementing CNN Using MNIST Dataset
  • 3.2.4 Gradient Adversarial Network [GAN]
  • 3.2.4.1 Fundamental Architecture of GAN
  • 3.2.4.2 Implementing GAN Using MNIST Dataset
  • 3.3 Research Perspective of Deep Learning
  • 3.3.1 Multi-Agent System: Argumentation
  • 3.3.2 Image Processor: Phenotyping
  • 3.3.3 Saliency-Map: Visualization
  • 3.4 Conclusion
  • References
  • Chapter 4 Web Service Composition Using an AI Planning Technique
  • 4.1 Introduction
  • 4.2 Background
  • 4.2.1 Introduction to AI
  • 4.2.2 AI Planning
  • 4.2.3 AI Planning for Effective Composition of Web Services
  • 4.3 Proposed Methodology for AI Planning-Based Composition of Web Services
  • 4.3.1 Clustering Web Services
  • 4.3.2 OWL-S: Semantic Markup for Web Services (For Composition Request)
  • 4.3.3 PDDL: Planning Domain Description Language
  • 4.3.4 AI Planner
  • 4.3.5 Flowchart of Proposed Approach
  • 4.4 Implementation Details
  • 4.4.1 Domain Used
  • 4.4.2 Case Studies on AI Planning
  • 4.4.2.1 Experiments and Results on Case 1 and Case 2
  • 4.5 Conclusions and Future Directions
  • References
  • Chapter 5 Artificial Intelligence in Agricultural Engineering
  • 5.1 Introduction
  • 5.2 Artificial Intelligence in Agriculture
  • 5.2.1 AI Startups in Agriculture
  • 5.2.2 Challenges in AI Adoption

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